Open AccessOn the diagnosis of radiative feedback in the presence of unknown radiative forcing
Author(s) -
Spencer Roy W.,
Braswell William D.
Publication year - 2010
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2009jd013371
Subject(s) - radiative forcing , radiative transfer , forcing (mathematics) , radiative flux , environmental science , cloud feedback , satellite , atmospheric sciences , atmospheric radiative transfer codes , physics , climate change , climate model , climate sensitivity , meteorology , geology , oceanography , aerosol , quantum mechanics , astronomy
The impact of time‐varying radiative forcing on the diagnosis of radiative feedback from satellite observations of the Earth is explored. Phase space plots of variations in global average temperature versus radiative flux reveal linear striations and spiral patterns in both satellite measurements and in output from coupled climate models. A simple forcing‐feedback model is used to demonstrate that the linear striations represent radiative feedback upon nonradiatively forced temperature variations, while the spiral patterns are the result of time‐varying radiative forcing generated internal to the climate system. Only in the idealized special case of instantaneous and then constant radiative forcing, a situation that probably never occurs either naturally or anthropogenically, can feedback be observed in the presence of unknown radiative forcing. This is true whether the unknown radiative forcing is generated internal or external to the climate system. In the general case, a mixture of both unknown radiative and nonradiative forcings can be expected, and the challenge for feedback diagnosis is to extract the signal of feedback upon nonradiatively forced temperature change in the presence of the noise generated by unknown time‐varying radiative forcing. These results underscore the need for more accurate methods of diagnosing feedback from satellite data and for quantitatively relating those feedbacks to long‐term climate sensitivity.